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Spatial predictions from individual and ensemble models

Source: R/adm_predict.R
adm_predict.Rd

This function allows the geographical prediction of one or more models constructed with the fit_ or tune_ function set, models fitted with esm_ function set (i.e., ensemble of small models approach), or models constructed with fit_ensemble function. It can return continuous or continuous and binary predictions for one or more thresholds

Usage

adm_predict(
  models,
  pred,
  training_data = NULL,
  nchunk = 1,
  predict_area = NULL,
  invert_transform = NULL,
  transform_negative = FALSE,
  sample_size = NULL
)

Arguments

models

list of one or more models fitted with fit_ or tune_ functions. In case use models fitted with fit_ensemble or esm_ family function only one model could be used. Usage models = mglm or models = list(mglm, mraf, mgbm)

pred

SpatRaster. Raster layer with predictor variables. Names of layers must exactly match those used in model fitting.

training_data

data.frame or tibble. Data used to fit the models. It is necessary to predict GAM and GLM models. Default NULL

nchunk

integer. Number of chunks to split data used to predict models (i.e., SpatRaster used in pred argument). Predicting models in chunks helps reduce memory requirements in cases where models are predicted for large scales and high resolution. Default = 1

predict_area

SpatVector, SpatialPolygon, or SpatialPolygonDataFrame. Spatial polygon used for restring prediction into only a given region. Default = NULL

invert_transform

named vector. Invert transformation of response variable. Useful for those cases that the response variable was transformed with one of the method in adm_transform. Usage = c(method = "anymethod", a = "transformation term a", b = "transformation term b"). Default NULL

transform_negative

logical. If TRUE, all negative values in the prediction will be set to zero. default FALSE.

sample_size

numeric. A vector containing the dimensions, in pixels, of raster samples. See cnn_make_samples beforehand. Default c(11,11)

Value

A list of SpatRaster with continuous and/or binary predictions

Examples

if (FALSE) {
require(dplyr)
require(terra)

data("sppabund")
envar <- system.file("external/envar.tif", package = "adm")
envar <- terra::rast(envar)

# Extract data
some_sp <- sppabund %>%
  dplyr::filter(species == "Species one") %>%
  dplyr::select(species, ind_ha, x, y)

some_sp

some_sp <-
  adm_extract(
    data = some_sp,
    x = "x",
    y = "y",
    env_layer = envar
  )

# Partition
some_sp <- flexsdm::part_random(
  data = some_sp,
  pr_ab = "ind_ha",
  method = c(method = "rep_kfold", folds = 3, replicates = 3)
)


## %######################################################%##
#                                                          #
####          Create different type of models           ####
#                                                          #
## %######################################################%##
# Fit some models
# require(gamlss)
# m1 <- gamlss::fitDist(some_sp$ind_ha, type="realline")
# m1$fits
# m1$failed
#
# m1 <- gamlss(ind_ha ~ pb(elevation) + pb(sand) + pb(bio3) + pb(bio12), family=NO, data=some_sp)
# choosen_dist <- gamlss::chooseDist(m1, parallel="multicore", ncpus=4, type="realAll")

mgam <- fit_abund_gam(
  data = some_sp,
  response = "ind_ha",
  predictors = c("elevation", "sand", "bio3", "bio12"),
  predictors_f = "eco",
  partition = ".part",
  distribution = gamlss.dist::NO()
)

mraf <- fit_abund_raf(
  data = some_sp,
  response = "ind_ha",
  predictors = c("elevation", "sand", "bio3", "bio12"),
  partition = ".part",
)

mgbm <- fit_abund_gbm(
  data = some_sp,
  response = "ind_ha",
  predictors = c("elevation", "sand", "bio3", "bio12"),
  partition = ".part",
  distribution =
  )


## %######################################################%##
#                                                          #
####            ' ####      Predict models              ####
#                                                          #
## %######################################################%##

# adm_predict can be used for predict one or more models fitted with fit_ or tune_ functions

# a single model
ind_p <- sdm_predict(
  models = mglm,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

# a list of models
list_p <- sdm_predict(
  models = list(mglm, mraf, mgbm),
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

# Predict an ensemble model
# (only is possilbe use one fit_ensemble)
ensemble_p <- sdm_predict(
  models = mensemble,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

# Predict an ensemble of small models
# (only is possible to use one ensemble of small models)
small_p <- sdm_predict(
  models = msmall,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

## %######################################################%##
#                                                          #
####              Predict model using chunks            ####
#                                                          #
## %######################################################%##
# Predicting models in chunks helps reduce memory requirements in
# cases where models are predicted for large scales and high resolution

ind_p <- sdm_predict(
  models = mglm,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL,
  nchunk = 4
)
}